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05/31/2023
Representation Reliability and Its Impact on Downstream Tasks
Self-supervised pre-trained models extract general-purpose representatio...
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05/31/2022
VQ-AR: Vector Quantized Autoregressive Probabilistic Time Series Forecasting
Time series models aim for accurate predictions of the future given the ...
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09/05/2021
Global-Local Item Embedding for Temporal Set Prediction
Temporal set prediction is becoming increasingly important as many compa...
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05/24/2021
One4all User Representation for Recommender Systems in E-commerce
General-purpose representation learning through large-scale pre-training...
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11/21/2020
A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting
Probabilistic time-series models become popular in the forecasting field...
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11/16/2020
Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning
We present a hierarchical planning and control framework that enables an...
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09/21/2020
div2vec: Diversity-Emphasized Node Embedding
Recently, the interest of graph representation learning has been rapidly...
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07/05/2020
Multi-Manifold Learning for Large-scale Targeted Advertising System
Messenger advertisements (ads) give direct and personal user experience ...
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06/26/2020
Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments
Graph representation learning is gaining popularity in a wide range of a...
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04/21/2020
A Neural Process Approach for Probabilistic Reconstruction of No-Data Gaps in Lunar Digital Elevation Maps
With the advent of NASA's lunar reconnaissance orbiter (LRO), a large am...
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07/24/2019
Tripartite Heterogeneous Graph Propagation for Large-scale Social Recommendation
Graph Neural Networks (GNNs) have been emerging as a promising method fo...
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03/14/2019
Online Gaussian Process State-Space Models: Learning and Planning for Partially Observable Dynamical Systems
Gaussian process state-space model (GPSSM) is a probabilistic dynamical ...
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09/19/2018
InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal Dynamics
The goal of system identification is to learn about underlying physics d...
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07/05/2018
Adaptive Path-Integral Autoencoder: Representation Learning and Planning for Dynamical Systems
We present a representation learning algorithm that learns a low-dimensi...
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07/05/2018
Adaptive Path-Integral Approach to Representation Learning and Planning for Dynamical Systems
We present a representation learning algorithm that learns a low-dimensi...
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06/21/2018
